文章基本信息

标题：On linearization of nonparametric deconvolution estimators for repeated measurements model
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作者：Daisuke Kurisu ; Taisuke Otsu
期刊名称：Distributional Analysis Publications
印刷版ISSN：1352-2469
出版年度：2021
卷号：2021
页码：1-23
语种：English
出版社：Suntory Toyota International Centres for Economics and Related Disciplines
摘要：By utilizing intermediate Gaussian approximations, this paper establishes asymptotic linear representations of nonparametric deconvolution estimators for the classical measurement error model with repeated measurements. Our result is applied to derive confidence bands for the density and distribution functions of the error-free variable of interest and to establish faster convergence rates of the estimators than the ones obtained in the existing literature.
关键词：measurement error;deconvolution;confidence band